From distraction to interaction: investigating learner engagement challenges in virtual classrooms by Irfan, Muhammad, Patel, Preeti, Hassan, Bilal

“…COVID-19, in particular, necessitated a global and swift adaptation by all teaching institutions to virtual learning methods. For universities, the transition to online learning predominantly focused on migrating teaching content, leaving online pedagogy, social interactions and informal learning areas, largely unattended. …”
Get full text

Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms by Richter, T, Shani, R, Tal, S, Derakshan, N, Cohen, N, Enock, PM, McNally, RJ, Mor, N, Daches, S, Williams, AD, Yiend, J, Carlbring, P, Kuckertz, JM, Yang, W, Reinecke, A, Beevers, CG, Bunnell, BE, Koster, EHW, Zilcha-Mano, S, Okon-Singer, H

“…This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training. …”

Glass box and black box machine learning approaches to exploit compositional descriptors of molecules in drug discovery and aid the medicinal chemist by Robson, B, Cooper, R

“…There are usually more inactive compounds by orders of magnitude, often a problem for machine learning methods. However, the approaches used here appear to work well for such “real world data”.…”

Active learning with applications in biomedical document annotation by Han, Xu

“…We also apply our active learning method for the task of named entity recognition. …”
Get full text

Output-weighted and relative entropy loss functions for deep learning precursors of extreme events by Rudy, Samuel H., Sapsis, Themistoklis P.

“…Such problems present a challenging task for data-driven modelling, with many naive machine learning methods failing to predict or accurately quantify such events. …”
Get full text

Portfolio Optimization Using a Hybrid Machine Learning Stock Selection Model by Masuda, Joshua S.

“…Additionally, two hybrid machine learning methods are used for prediction: CNN-LSTM and BiLSTM-BO-LightGBM. …”
Get full text

Forgery localization in images by Nur Dilah Binte Zaini

“…Late fusion is implemented to combine the confidence scores of the predicted class for each classifier. Simple machine learning methods have been carried out to implement image forgery detection and deep fake detection in this paper. …”
Get full text

Deep features based real-time SLAM by Syed Ariff Syed Hesham

“…This project implements a near real-time stereo SLAM system designed to operate effectively in extreme conditions using Deep Learning methods. It employs a Parallel Tracking-and-Mapping approach, making use of stereo constraints to ensure robust initialization and accurate scale recovery while maintaining real-time performance. …”
Get full text

Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach by Nor Surayahani Suriani, Nor Surayahani Suriani, Ahmad Tarmizi, Syaidatus Syahira, Hj Mohd, Mohd Norzali, Mohd Shah, Shaharil

“…Most of the deep learning methods require devices with high computational resources which hardly implemented in mobile applications. …”
Get full text

Multimodal deception detection in videos by Syazwan Bin Jainal

“…There have been many approaches to the problem of deception detection, which include psychological, physiological and even machine learning methods. Deception detection has been successful in high-stakes situations, like courtrooms, where subjects are put under a stressful situation and experiments have yielded an accuracy of over 90%. …”
Get full text

Mobile robots autonomous exploration through deep reinforcement learning by Yin, Hanqiu

“…The algorithm adopts a deep learning method to effectively extract the environment features and automatically updates the strategy by interacting with the environment. …”
Get full text

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra by Schaeffer, Joachim, Gasper, Paul, Garcia-Tamayo, Esteban, Gasper, Raymond, Adachi, Masaki, Pablo Gaviria-Cardona, Juan, Montoya-Bedoya, Simon, Bhutani, Anoushka, Schiek, Andrew, Goodall, Rhys, Findeisen, Rolf, Braatz, Richard D, Engelke, Simon

“…We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. …”
Get full text

Learning driver-specific behavior for overtaking : a combined learning framework by Lu, Chao, Wang, Huaji, Lv, Chen, Gong, Jianwei, Xi, Junqiang, Cao, Dongpu

“…However, traditional offline learning methods lack the ability to adapt to individual driving behavior. …”
Get full text

Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning by Zhang, Runhong, Wu, Chongzhi, Goh, Anthony Teck Chee, Böhlke, Thomas, Zhang, Wengang

“…Surrogate models were developed via ensemble learning methods (ELMs), including the eXtreme Gradient Boosting (XGBoost), and Random Forest Regression (RFR) to predict the maximum lateral wall deformation (δhmax). …”
Get full text

Using machine learning to generate novel hypotheses: increasing optimism about COVID-19 makes people less willing to justify unethical behaviors by Sheetal, Abhishek, Feng, Zhiyu, Savani, Krishna

“…The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.…”
Get full text

AI assisted indoor localization by Lee, Yih Jie

“…In this report, the obstacles that Wi-Fi fingerprinting and traditional machine learning methods face will be overcome by relying on deep learning approaches. …”
Get full text

Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification by Wu, Guoqiang, Ning, Xin, Hou, Luyang, He, Feng, Zhang, Hengmin, Shankar, Achyut

“…The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. …”
Get full text

Social media sentiment enhanced stock market prediction analysis by Tan, Adrian Yong Chang

“…The result implies that for classification, ensemble learning methods tend to perform better in terms of accuracy, while SVM tend to perform better in terms of F-Measure. …”
Get full text

Looking deep at people: towards understanding and generating humans in images with deep learning by de Bem, RA

“…</p> <p>This thesis pursues further advances towards understanding and generating people in visual data by the development of new discriminative and generative deep learning methods. The main contributions are: </p> <p>i) A deep learning framework for 2D human pose estimation, which allows for mean-field inference over part-based models; </p> <p>ii) A conditional deep generative model that achieves state-of-the-art results on generating images of humans conditioned on body posture; and </p> <p>iii) A structured semi-supervised deep generative model that jointly performs pose estimation and image generation, <em>understanding</em> and <em>generating</em> people in images in a single framework.…”

Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network by Asif, M. T., Oran, A., Fathi, E., Xu, M., Dhanya, M. M., Mitrovic, N., Jaillet, P., Dauwels, Justin, Goh, Chong Yang

“…Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. …”
Get full text
Get full text